Directing Sampling based on Uncertainty Analysis
Bibliographic record
Abstract
Determining where and whatto sample for environmental modeling of receiving waters is becoming increasingly important because the need for improved accuracy in model results conflicts with limited site sampling budgets.A quantitative approach to sampling, entitled Quantitatively Directed Exploration (QDE), provides a mathematical framework for determining the best location to sample, and what parameter should be sampled.QDE employs a first-order Taylor series expansion to estimate the uncertainty or variance in the model results.Uncertainty in input parameters is determined through data extrapolation teclmiques, specifically multivariate conditional probability, while model sensitivity is calculated by directly coding sensitivity derivatives into a model using ADIFOR 2.0.Combining these two matrices produces the variance in model results, which in tum is employed to direct sampling.The next sampling location is defined as the point where the variance in model results is the largest.\Vhich input parameter to sample is detennined by evaluating the conttibution to the total variance produced by each input parameter.The QDE approach is demonstrated on a water quality model where non-point source loading, stream characteristics, and contaminant behavior are uncertain input parameters and concentration is the uncertain model result.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".